Papers proposing optimization schemes

This paper introduces an Articial Intelligence (AI) for a Reviewer Assignment Problem (RAP) consisting in assigning a review task to each of the authors of a conference. The AI for RAP consists of an Information Retrieval step and an Expertise Matching step. The main contribution of this paper is in casting a novel Convex Expertise Matching (ConvEM) scheme for large scale assignments. ConvEM is based on splitting the Expertise Matching problem in convex sub-problems with equal number of reviewers and papers. The introduced AI for RAP is tested in a case study for a conference with 3051 authors and 1360 papers. The performance of ConvEM is evaluated by comparison with a greedy assignment used as baseline. Finally, this paper discusses the large potential to adapt ConvEM to e.g. i) deal with more generic RAP problems with author quotas and reviewer quotas, and ii) incorporate other research results such as advances in Information Retrieval.

This paper presents a complete method for au- tomatic and robust control configuration selection for linear systems which relies upon acquired process data under gaussian noise excitation. The selection of the configuration is based on the estimation of the Interaction Measure named Participation Matrix. This estimation is derived with uncertainty bounds, which allows to determine online whether the uncertainty is sufficiently low to derive a robust decision on the control configuration to be used or if the uncertainty should be reduced by e.g. prolonging the experiment to obtain more data.